The BI-RADS (Breast Imaging-Reporting and Data System) classification has been devised, in particular, by the American college of Radiology initially to standardize the reporting of the visual analysis of mammograms, i.e. X-Ray pictures of breasts, by radiologists in the frame of the assessment of breast cancer and breast cancer risk. The classification has then been extended to magnetic resonance imaging (MRI) and ultrasound pictures. Two types of categories are used, the assessment categories and the breast density categories.
The BI-RADS assessment categories are used to grade the pathological status of the breast being the subject of the mammogram:    0: Incomplete (the mammogram cannot be graded)    1: Negative (no tumor, either benign or malignant)    2: Benign finding    3: Probably benign    4: Suspicious abnormality    5: Highly suggestive of malignancy    6: Proven Malignancy (known from biopsy)
The BI-RADS breast density categories are used to evaluate the density of non pathogenic breasts:    1: Almost entirely fatty (i.e. non dense), which means that fibroglandular tissues make up less than 25% of the breast;    2: Scattered fibroglandular densities, which means that fibrous and glandular tissue makes up from 25 to 50% of the breast;    3: Heterogeneously dense, which means that the breast has more areas of fibrous and glandular tissue (from 51 to 75%) that are found throughout the breast;    4: Extremely dense, which means that the breast is made up of more than 75% fibroglandular tissue.
Variations in density from one breast to another are due to differences in fat/fibroglandular tissue proportions. Fatty tissues appear as non-dense areas in mammograms whereas fibroglandular tissues appear as dense areas. Mammographic density is thus a measure of the fibroglandular or non-fatty tissue of the mammogram. Various classifications, in addition to the BI-RADS classification, can be used for characterizing breast density. As such the classification of Wolfe uses 4 parenchymal patterns, N1: almost entirely fatty, P1 linear densities (enlarged galactophoric ducts) occupying no more than 25% of the breast, P2: linear densities (from enlarged galactophoric ducts) occupying more than 25% of the breast, and DY: dense, radiopaque breast. Percent dense area, or 2D percent density, defined as the ratio of the projected area of dense breast tissue divided by area of the entire breast, is a computerized method that is also used.
Mammographic density has been established as a key factor risk for breast cancer, with denser mammograms associated with an odds ratio of at least 4 for breast cancer, i.e. mammograms either graded 4 according to the BI-RADS classification of breast composition or DY according to the parenchymal pattern classification or having a percent density above 75%.
Although these classification methods yield somewhat similar results in terms of breast cancer prediction—percent dense area even yielding slightly better results—the BI-RADS classification is still today more widely used in clinical practice throughout the world because it is easier to determine and report.
This classification however suffers from one major drawback or bias, which is linked to the subjectivity of the method, i.e. it is strongly operator-dependant. In fact even agreement exhibited by individual radiologists vary widely with intra-radiologist percent agreement ranging from 62.1% to 87.4% (Spayne (2012) Breast J. 18:326-33). Moreover, there appears to be an overestimation of density by radiologists as compared to a computerized determination of density (Ciatto et al. (2012) Breast 21:503-506).
Computer-assisted classification methods have thus been developed in order to improve the reproducibility of mammogram grading according to the BI-RADS classification. In this regard, the Volpara Imaging software (Aitken et al. (2010) Cancer Epidemiol. Biomarkers Prev. 19:418-428) and the Hologic Quantra software (Ciatto et al. (2012) Breast 21:503-506) both analyze digital mammograms in a fully automated volumetric fashion and produce a quantitative assessment of breast composition, namely volume of fibroglandular tissue in cubic centimeters, volume of breast tissue in cubic centimeters, and their ratio (i.e. volumetric density or 3D percent density). However, these methods tend to under-evaluate breast density with respect to human classifying reader. As such, Ciatto et al. (2012) Breast 21:503-506 report that breast density assessed by computer using the Quantra software provided systematically lower percentage values as compared to visual classification.
It is thus an object of the present invention to provide a computer-based method to assist human operators, e.g. radiologists, in classifying mammograms according to the BI-RADS classification, which more accurately reflects visual classification assessed by experienced human classifying readers.